Estimating Uncertainty in Galaxy Morphology Classification

arXiv:2608.08398 · cs.AI, astro-ph.IM · Submitted 2026-08-09 · Read on arXiv

Kai Cheng, Ruoqi Wang, Qiong Luo

cs.AI, astro-ph.IM

Submitted: 2026-08-09

Updated: 2026-08-11

Code: https://github.com/henrysky/Galaxy10

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• AUC — 35× in this paper · explained in 2 episodes
• Foundation Model — 29× in this paper · explained in 3 episodes
• Foundation Models — 23× in this paper · explained in 8 episodes
• Aleatoric Uncertainty — 13× in this paper · explained in 7 episodes
• Uncertainty Quantification — 10× in this paper · explained in 9 episodes
• F1 Score — 9× in this paper · explained in 4 episodes
• Galaxy Morphology Classification — 9× in this paper
• Epistemic Uncertainty — 6× in this paper · explained in 12 episodes
• Artificial Intelligence — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Uncertainty Estimation for Galaxy Morphology Classification — 3× in this paper
• Area Under Curve — 2× in this paper
• Fisher Information — 2× in this paper · explained in 4 episodes
• Representation Learning — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Transformers — 2× in this paper · explained in 2 episodes
• Uncertainty Decomposition — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 9 episodes
• Computational Efficiency — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Gating Mechanism — 1× in this paper · explained in 2 episodes
• Performance Gap — 1× in this paper · explained in 2 episodes
• Quantifying Uncertainty — 1× in this paper · explained in 5 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 6 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 16 episodes
• Vision Foundation Models — 1× in this paper · explained in 2 episodes

Sources

• Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference

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